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Machine Learning for Health Informatics Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Machine Learning and Data Mining Methods for Managing Parkinson’s Disease

Authors : Dragana Miljkovic, Darko Aleksovski, Vid Podpečan, Nada Lavrač, Bernd Malle, Andreas Holzinger

Published in: Machine Learning for Health Informatics

Publisher: Springer International Publishing

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Abstract

Parkinson’s disease (PD) results primarily from dying of dopaminergic neurons in the Substantia Nigra, a part of the Mesencephalon (midbrain), which is not curable to date. PD medications treat symptoms only, none halt or retard dopaminergic neuron degeneration. Here machine learning methods can be of help since one of the crucial roles in the management and treatment of PD patients is detection and classification of tremors. In the clinical practice, this is one of the most common movement disorders and is typically classified using behavioral or etiological factors. Another important issue is to detect and evaluate PD related gait patterns, gait initiation and freezing of gait, which are typical symptoms of PD. Medical studies have shown that 90% of people with PD suffer from vocal impairment, consequently the analysis of voice data to discriminate healthy people from PD is relevant. This paper provides a quick overview of the state-of-the-art and some directions for future research, motivated by the ongoing PD_manager project.

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previous chapter Argumentation for Knowledge Representation, Conflict Resolution, Defeasible Inference and Its Integration with Machine Learning
next chapter Challenges of Medical Text and Image Processing: Machine Learning Approaches
Footnotes
2
Standard distance: a statistic mainly used for spatial GIS data, to measure compactness of a distribution.
 
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Metadata
Title
Machine Learning and Data Mining Methods for Managing Parkinson’s Disease
Authors
Dragana Miljkovic
Darko Aleksovski
Vid Podpečan
Nada Lavrač
Bernd Malle
Andreas Holzinger
Copyright Year
2016
Book
Machine Learning for Health Informatics

Print ISBN: 978-3-319-50477-3

Electronic ISBN: 978-3-319-50478-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-50478-0_10

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